To successfully market products and services, manufacturers and distributors must make numerous technical and financial decisions related to technical and financial factors. In particular, these manufacturers and distributors, referred to hereinafter as “marketers,” must appropriately set prices when introducing new products, properly brand these new products, select effective packaging, size, and other visual attributes, choose optimal geographic areas for introducing new products, etc. Additionally, markets must allocate reasonable amounts of resources (e.g., money, personnel) to advertisement, as well as properly select the timing and duration of consumer and trade promotions. Although these decisions are relevant in marketing of both existing and new products, the difficulties of introducing products into a market are particularly severe. According to some studies, as much as 99% of new products in the food industry fail within one year of introduction.
For these reasons, marketers must understand the underlying dynamics of the markets in which products and services compete. These dynamics may be understood in terms of the structure of the market and the consumer switching tendencies (i.e., trends in purchasing or using one product instead of another). The structure of a market may, in turn, correspond to what consumers consider to define a particular set of choices for a potential purchase. Products and services accordingly compete within a particular set of choices in view of various factors such as perceived quality, price, overall attractiveness, etc. In other words, the general perception of which products and services compete with each other and which substitutions are possible defines the landscape of competition.
Traditionally, marketers, investors, economists, and other observers have relied on econometric modeling, consumer surveys, and tracking purchases over time to understand market dynamics. However, each of these approaches yields only a very a limited understanding of the market. Using an econometric approach, for example, observers analyze the impact of the manufacturers' commercial activities on the share price or the sales volume. In accordance with econometric theories, it is possible to measure the impact of a particular sales promotion and derive data from these measurements useful in planning the future strategy. However, this approach is largely unsuitable in situations that involve multiple product categories, hundreds of brands, and/or large numbers of stock keeping units (SKUs).
On the other hand, some marketers collect consumer survey data to determine how the consumers view a particular market. A written, online, or an oral survey may ask, for example, whether the consumer considers a certain item to be a potential replacement for another item, and how close the consumer considers the two items to be. The surveys thus seek consumers' opinions regarding the structure and, to some extent, the mechanics of competition. However, consumer surveys frequently are very superficial generally fail to reflect actual switching behavior. Further, surveyed consumers typically respond to only a subset of relevant factors such as visual characteristics, for example, and may not have access to other factors affecting switching such as taste.
In accordance with another known approach, marketers track consumer behavior over a certain period of time. Inherent in this approach is the premise that a user will switch to other items according to the way he or she mentally defines the set of competing items. However, it is impossible to know from this data whether the switching is due to direct substitution (e.g., one make of a car instead of another) or a complementary relationship (e.g., purchasing a car instead of using mass transit). In most industries, the differences tend to be evolutionary and small, and precision is therefore a critical requirement.
Thus, most known techniques are largely based either on guesswork or on a trial-and-error approach. These techniques generally fail to yield correct understanding of how products and services complete, particularly in densely saturated markets such as the soft drink market, for example, in which manufacturers offer hundreds of brands in a variety of flavors, sizes, types of packaging, etc.
Recently, attempts have been made to apply advanced statistical methods to model markets and ultimately improve the quality of predictions. In particular, hierarchical models of market segmentation provide a structured view of a particular market. A hierarchical model describes the market as a nested structure having one or several items within a partition on a particular level, with a further restriction that any one item belongs to only one partition at a given level. The hierarchical modeling approach stipulates that switching is more probable among items that belong to the same partition.
Some theories of hierarchical modeling propose a probabilistic view of brand switching and assert that for directly competing brands (i.e., brands within the same market partition), the level of switching among these brands is proportional to the product of the respective market shares of these brands. Further, some of these theories postulate that the exact level of switching between brands (sometimes referred to as the “proportionality constant”) is the value that maximizes the randomness of information related to brand shares, i.e., entropy of the system. In other words, techniques consistent with such theories of hierarchical modeling attempt to predict how often consumers will switch between items within a certain partition given the specific market shares of these items.
Applying a hierarchical model, marketers attempt to develop a comprehensive view of the competitive framework. However, while the market share of a particular item may be relatively easy to obtain from the sales data available for the item and the competing items, placing the item within the hierarchical structure is a matter of hypothesizing a certain market structure. As a result, many of the problems discussed above such as subjective assessment of competitive sets, for example, persist in hierarchical modeling.
Moreover, to the extent that software tools support hierarhical modeling at all, these tools lack the convenience of user interface that is essential in modeling complicated multi-attribute and multi-brand markets. Some statistical and mathematical tools, for example, offer a text-based interface that requires that the user master a number of relatively complicated commands to define and test market structures. Further, the available tools lack the flexibility required for accurate prediction of consumer behavior.